Course: 2020/2021

Mathematics for data analysis

(17228)

Students are expected to have completed

Proficiency in high school mathematics

While there are many applied mathematics techniques and concepts that are useful (and used) in the Big Data analysis context, this course focus on the basics of those based on linear algebra, as it underlies many of the most importants applications and algorithms. Thus, the course is intended to understand the mathematical ideas behind those applications and algorithms (usually implemented in black-box software) so practitioners have a deeper knowledge of the results arising from them, allowing for a better interpretation.

Description of contents: programme

1. Linear Systems
2. Vectors
3. Matrices
4. Diagonalization
5. Orthogonality
6. Symmetric Matrices

Learning activities and methodology

Theoretical classes (lectures)
Practical problems that students must solve individually as homework
Tutorials

Assessment System

- % end-of-term-examination 100
- % of continuous assessment (assigments, laboratory, practicals...) 0

Basic Bibliography

- David C. Lay, Steven R. Lay, Judi J. McDonald. Linear Algebra and Its Applications. Pearson; 5 edition. 2016

- Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong · Mathematics for Machine Learning : https://mml-book.github.io/

Additional Bibliography

- W. Keith Nicholson. Linear Algebra with Applications. McGraw-Hill, 6th edition. 2009

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